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saidby

Offline-first diarized transcription: a command-line tool and Python library that turns any recording into a transcript with speakers, timestamps, glossary-aware spelling and one audio track per speaker.

Status: pre-alpha. Version 0.0.1 ships the CLI skeleton (--version, doctor, models). The processing pipeline arrives in 0.1.0.

Install

uv tool install saidby

For machines without an NVIDIA GPU, avoid downloading the CUDA build of PyTorch:

uv tool install saidby --index https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match

Requirements: ffmpeg on the PATH and, for GPU use, an NVIDIA driver 580 or newer.

Models

Models are never bundled and are only downloaded by an explicit command:

saidby models list
saidby models pull --all

Processing always runs offline (HF_HUB_OFFLINE=1). A Hugging Face token (HF_TOKEN) is needed only for gated models such as pyannote/speaker-diarization-community-1; if the conditions have not been accepted, the error shows the URL where to accept them.

Privacy

  • No telemetry leaves the machine. Logs and telemetry are written locally and never contain transcript text, names, glossary terms or voiceprints.
  • A test suite blocks any non-loopback network access during processing.

License

Apache-2.0. See LICENSE, NOTICE and MODEL_LICENSES.md. Contributions are accepted under the Developer Certificate of Origin.

Metadata

Release files for saidby 0.0.1

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saidby-0.0.1-py3-none-any.whl Python 3 none any Details

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0.4.4

2 release files

0.4.3

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0.4.2

2 release files

0.4.1

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0.4.0

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0.1.0

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0.0.1 This release

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